Mapping A Maze Using MATLAB Image Processing Toolbox

Recently I saw an article about mapping a maze and I thought to myself; how could this be achieved using my favorite tool MATLAB :-)? Well it turns out that its a pretty simple task. Some functions used in this post require that you have MATLAB 2012a or higher installed, so if u do not then go away and get it.

Wow you are back, ok so lets begin, first we will look at the code that was used and then further down we will look at individual functions and what they were used to do.

The maze:

The code:

%% Map Maze
im = imread('maze.png');
bw = im2bw(im(1:287, 1:400), 0.45);
cc = bwconncomp(bw, 8);
obj = false(size(bw));
obj(cc.PixelIdxList{13}) = true;
sln = bwmorph(bwmorph(obj,'thin',Inf),'spur', Inf);
figure, imshow(imfuse(im,sln,'blend','Scaling','joint'))

clear Clears all variables from the MATLAB workspace.
im = imread(‘maze.png’);
Reads the maze image and stores it.
bw = im2bw(im(1:287, 1:400), 0.45); Converts the image to binary, scales it and stores it.
cc = bwconncomp(bw, 8); Find connecting components within the image.
obj(cc.PixelIdxList{13}) = true; Find the largest component and select it.
sln = bwmorph(bwmorph(obj,’thin’,Inf),’spur’,Inf); Use Morphological functions to refine image.
figure, imshow(imfuse(im,sln,’blend’,’Scaling’,’joint’)); Overlay and display images


solved maze

The end 🙂


Image Classification Using MATLAB SOM/LVQ

I like to think of myself as a hacker :-), not in today’s sense of the word [person who breaks into secured computer areas] but as a hacker in the sense of first two definitions found here. I like to experiment with things especially related to computers and Artificial Intelligence in particular. MATLAB happens to be a virtual toolbox for the trade pun intended  :-), using some of its toolboxes we will see how we can solve a ubiquitous problem that faces most persons with a nice camera and a voracious appetite for taking pictures.

Now, I don’t have an expensive Nikon, but I do have loads of pictures; and one day i was trying to find this particular picture when it occurred to me that if i could not remember the name or date when i took the picture  it would require me to search blindly every picture I had in order to find that special one. Now what if i had some way of finding the picture based on what i could remember of it? ie. environment in which it was taken, colours and objects along with some other visual specifications, wouldn’t that be cool.

So I went for the proverbial toolbox MATLAB, which tools will I need?

  1. Neural Network    -> selforgmap, lvqnet, vec2ind, ind2vec
  2. Image Processing -> imhist, imresize, rgb2gray, imread


  1. mspaint

Note: For this demonstration I will be using:

  • MATLAB R2011b on Windows 7
  • Pictures found at  C:\Users\Public\Pictures\Sample Pictures

Ok lets do it,  start up MATLAB, copy and paste all the pics from the above directory to MATLAB’s current directory. {Chrysanthemum, Desert, Hydrangeas, Jellyfish, Koala, Lighthouse, Penguins, Tulips}.

  1. In the new edit window copy and paste the code as given below. Save file as scan.m
    function scan(img)
    files = dir('*.jpg');
    hist = [];
    for n = 1 : length(files)
        filename = files(n).name;
        file = imread(filename);
        hist = [hist, imhist(rgb2gray(imresize(file,[ 50 50])))]; %#ok
    som = selforgmap([10 10]);
    som = train(som, hist);
    t   = som(hist); %extract class data
    net = lvqnet(10);
    net = train(net, hist, t);
    like(img, hist, files, net)

    Links to the functions used were provided above therefore i will not be going into the details of how they work, however there is a narrative with regard to the workings of the code:

    The code starts by searching the current MATLAB directory for all files with a .jpg extension. On each iteration of the loop an image is loaded and resized to 50 x 50, it is then converted to greyscale and a histogram measurement is taken of its pixels [feature vector]; the results are then appended to a 256 x n matrix with n been the number of images scanned.

    A self organizing map network is then used to identify classes into which the images fall. The feature matrix and class data is used to train a Learning Vector Quantization neural network, that will be used for classification of images presented to it.

  2. Next we will create a function to display all matching images for an image we submit to the LVQ network.
    function like(im, hist, files , net)
        hs = imhist(rgb2gray(imresize(im,[50 50])));
        cls = vec2ind(net(hs));
        [~, n] = size(hist);
        for i = 1 : n
            if(cls == vec2ind(net(hist(:, i))))
                figure('name', files(i).name);
                imshow(imresize(imread(files(i).name), [100 100]))
  3. Download a picture of a koala and save it outside your MATLAB path as koalatest.jpg
  4. At the MATLAB command prompt type scan(imread(‘[replace with path to koalatest]\koalatest.jpg’)
  5. After a minute or two the networks should have been trained and a figure displaying the matching koala.jpg image shown to you.

NOTE: As explained above this is hacking, not production code I wrote this up in about 20 minutes as a demonstration for classification of images, with imagination this could be extended to classify things like sound for example using a feature map crated from humming a tune to find a song with a similar melody.

LVQ Been trained:

Image Slicing Using MATLAB

MATLAB is an awesome tool for image processing and analysis; one feature that puts it head and shoulders above the rest is it’s ability to represent images as a matrix. With this capability we can leverage our knowledge of Linear Algebra and Calculus to do some pretty amazing things. This use of matrices also allows us to intuitively perform some simple functions such as cutting an image into multiple pieces.

To do this we first load an image into MATLAB using the imread command.

waldo = imread('');

To take just a block of the image for further analysis by either an image recognition algorithm, neural net, or some other function we would use this code.


where imshow(xLbound : xUbound, yLbound : yUbound, : ) we take the entire row and column for the last parameter to preserve the colour of the image. See if you can identify where the slice came from. 🙂